Towards network-wide QoE fairness using openflow-assisted adaptive video streaming

Panagiotis Georgopoulos, Yehia Elkhatib, Matthew Broadbent, Mu Mu, Nicholas Race

Research output: Contribution to Book/ReportChapterpeer-review

Abstract

Video streaming is an increasingly popular way to consumemedia content. Adaptive video streaming is an emerging de-livery technology which aims to increase user QoE and max-imise connection utilisation. Many implementations naivelyestimate bandwidth from a one-sided client perspective, with-out taking into account other devices in the network. Thisbehaviour results in unfairness and could potentially lowerQoE for all clients. We propose an OpenFlow-assisted QoEFairness Framework that aims to fairly maximise the QoEof multiple competing clients in a shared network environ-ment. By leveraging a Software Dened Networking tech-nology, such as OpenFlow, we provide a control plane thatorchestrates this functionality. The evaluation of our ap-proach in a home networking scenario introduces user-levelfairness and network stability, and illustrates the optimisa-tion of QoE across multiple devices in a network.
Original languageEnglish
Title of host publicationProceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking - FhMN '13
Place of PublicationNew York, New York, USA
PublisherAssociation for Computing Machinery (ACM)
Pages15
ISBN (Print)9781450321839
DOIs
Publication statusPublished - 2013

Publication series

NameProceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking - FhMN '13

Fingerprint Dive into the research topics of 'Towards network-wide QoE fairness using openflow-assisted adaptive video streaming'. Together they form a unique fingerprint.

Cite this